System for evaluating potential claim outcomes using related historical data

ABSTRACT

A computer system includes a data storage device. The data storage module receives, stores, and provides access to historical claim data. A categorical aggregation component comprising program instructions stored in a program memory provides categorized and aggregated historical claim data by identifying claim categories based on the historical claim data, each of the one or more claim categories associated with a respective set of claim characteristics, identifying claims of the historical claim data associated with one of the claim categories, assigning each identified claim to one of a plurality of total severity ranges based on the total severity of the identified claim, and determining an average cost per claim year for claims of each total severity range when executed by a computer processor.

CROSS-REFERENCE TO RELATED APPLICATION

This is a continuation application of prior co-pending U.S. applicationSer. No. 12/466,016, filed Jun. 14, 2009, which is incorporated hereinby reference.

FIELD

Embodiments relate to computer systems to categorize and aggregatehistorical claim data. Embodiments are also concerned with theevaluation of current claims based on categorized and aggregatedhistorical claim data.

BACKGROUND

Insurance claims having multiple potential outcomes are difficult toevaluate. In the case of medical claims, for example, the extent of aclaimant's future medical recovery is both indeterminate and inverselyrelated to total claim cost (i.e., total severity).

According to existing techniques for evaluating a claim having multiplepotential outcomes, an adjuster determines a most probable outcome basedon her professional experience and based on the file history of thesubject claim. A total severity is then estimated based on thedetermined outcome. The adjuster may use the estimated total severity asa basis for adjusting insurance reserves, suggesting treatment options,reallocating resources (e.g., triggering a nursing assignment orphysician review), and/or re-assigning the claim to a more (or less)senior adjuster.

Conventional evaluation techniques do not provide a robust system todetermine relative likelihoods of potential outcomes in order toidentify a most probable outcome. Conventional techniques also exhibitdifficulty in estimating the total severity of an identified mostprobable outcome. Moreover, any actions performed by an adjuster basedon the estimated total severity fail to take into account outcomes whichpresent a lower probability but a higher exposure than the determinedmost probable outcome, and outcomes which present a lower probabilityand a lower exposure than the determined most probable outcome.Consequently, improvements in any of the foregoing deficiencies mayimprove the quality of these actions (e.g., reserve adjustments,treatment suggestions and claim reassignments) to the benefit of theclaimant and the insuring entity.

SUMMARY

A computer system is disclosed which includes a data storage device.Functions performed by the data storage module include receiving,storing and providing access to historical claim data. A computerprocessor may execute program instructions and retrieve the historicalclaim data from the data storage device, while a memory, coupled to thecomputer processor, stores program instructions for execution by thecomputer processor.

A categorical aggregation component includes program instructions storedin the program memory, and provides categorized and aggregatedhistorical claim data by identifying claim categories based on thehistorical claim data, where each of the one or more claim categoriesassociated with a respective set of claim characteristics, identifyingclaims of the historical claim data associated with one of the claimcategories, assigning each identified claim to one of a plurality oftotal severity ranges based on the total severity of the identifiedclaim, and determining an average cost per claim year for claims of eachtotal severity range when executed by the computer processor.

The computer system also includes a communication device, coupled to thecomputer processor, to output the categorized and aggregated historicalclaim data including the average cost per claim year for claims of eachtotal severity range.

According to some embodiments, a computer system includes a data storagedevice for receiving, storing, and providing access to categorized andaggregated historical claim data, a computer processor for executingprogram instructions and for retrieving the categorized and aggregatedhistorical claim data from the data storage device, and a memory,coupled to the computer processor, for storing program instructions forexecution by the computer processor.

A claim evaluation component includes program instructions stored in theprogram memory. The claim evaluation component is for receiving claimdata of a current claim, identifying a pre-defined claim category basedon the current claim data, calculating an estimated cost of the currentclaim for each of a plurality of outcome scenarios based on historicalclaim data associated with the pre-defined claim category, anddetermining a likelihood associated with each of the plurality ofoutcome scenarios based on the categorized and aggregated historicalclaim data associated with the pre-defined claim category when executedby the computer processor.

An output device, coupled to the computer processor, may present agraphical representation of the estimated cost and the likelihoodassociated with each of the plurality of outcome scenarios. The computerprocessor generates the graphical representation in accordance withprogram instructions in the program memory and executed by the computerprocessor.

With these and other advantages and features that will become apparent,embodiments may be more clearly understood by reference to the followingdetailed description, the appended claims, and the drawings attachedhereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system architecture within which some embodimentsmay be implemented.

FIG. 2 is a partial functional block diagram of a computer systemprovided in accordance with some embodiments.

FIG. 3 is a flow diagram of a process according to some embodiments.

FIG. 4 illustrates categorized and aggregated historical claim cost dataaccording to some embodiments.

FIG. 5 illustrates categorized and aggregated historical claim countdata according to some embodiments.

FIG. 6 is a partial functional block diagram of a computer systemprovided in accordance with some embodiments.

FIG. 7 is a flow diagram of a process according to some embodiments.

FIG. 8 is a outward view of a representative interface to receivecurrent claim data according to some embodiments.

FIG. 9 is a graphical representation of graphical representation of anestimated cost and a likelihood associated with each of a plurality ofoutcome scenarios for a first claim category according to someembodiments.

FIG. 10 illustrates categorized and aggregated historical claim countdata according to some embodiments.

FIG. 11 is a graphical representation of graphical representation of anestimated cost and a likelihood associated with each of a plurality ofoutcome scenarios for the first claim category according to someembodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates system architecture 100 within which some embodimentsmay be implemented. Although the devices of architecture 100 aredepicted as communicating via dedicated connections, it should beunderstood that all illustrated devices may communicate to one or moreother illustrated devices through any number of other public and/orprivate networks, including but not limited to the Internet. Two or moreof the illustrated devices may be located remote from one another andmay communicate with one another via any known manner of network(s)and/or a dedicated connection. Moreover, each device may comprise anynumber of hardware and/or software elements suitable to provide thefunctions described herein as well as any other functions. Othertopologies may be used in conjunction with other embodiments.

According to the example of FIG. 1, data entry terminals 110 receiveclaim data associated with various insurance claims. The foregoingdescription provides examples relating to medical insurance claims butembodiments are limited thereto. Data entry terminals 110 may beoperated by a billing entity (e.g., a hospital, a physician's office, apharmacy) or by a claims processor entering data received from a billingentity. Any number of data entry terminals may be employed to receiveclaim data according to some embodiments. The claim data may begenerated and received using any systems that are or become known.

The claim data may include patient identification information, a servicedate, a description (e.g., of a procedure or a drug), a billing code(CPT ICD-9, Rev Codes, HCPCS), and a cost. The claim data may includeany other suitable information related to a claim. Examples includepatient height, patient weight, health risks and diagnosis.

The claim data is received and stored by data warehouse 120. Any numberor type of data storage systems may store the claim data in any suitablemanner according to some embodiments. Non-exhaustive examples include arelational database system, a spreadsheet, and any other data structurethat is amenable to parsing.

Categorical aggregation component 130 receives the claim data(hereinafter referred to as “historical claim data”) from data warehouse120. Categorical aggregation component 130 categorizes and aggregatesthe historical claim data according to some embodiments. Detailedexamples of this operation are provided below. Briefly, categoricalaggregation component 130 identifies claim categories based on thehistorical claim data, where each of the one or more claim categoriesassociated with a respective set of claim characteristics. Claims of thehistorical claim data which are associated with one of the claimcategories are identified, each identified claim is assigned to one of aplurality of total severity ranges based on the total severity of theidentified claim, and an average cost per claim year is determined forclaims of each total severity range.

Categorical aggregation component 130 may comprise any combination ofhardware and/or processor-executable instructions stored on a tangiblemedium. According to some embodiments, categorical aggregation component130 is an component of data warehouse 120 or data storage device 140.

Data storage device 140 may comprise any entity to store data, includinga simple file server. In the illustrated embodiment, data storage device140 receives and stores data output by categorical aggregation component130. Specifically, data storage device 140 may store categorized andaggregated historical claim data including the average cost per claimyear for claims of each total severity range.

Claim evaluation component 150 may access the categorized and aggregatedhistorical claim data of data storage device 140. Claim evaluationcomponent 150 is for receiving claim data of a current claim,identifying a pre-defined claim category based on the current claimdata, calculating an estimated cost of the current claim for each of aplurality of outcome scenarios based on historical claim data associatedwith the pre-defined claim category, and determining a likelihoodassociated with each of the plurality of outcome scenarios based on thecategorized and aggregated historical claim data associated with thepre-defined claim category when executed by the computer processor.Detailed examples of the foregoing according to some embodiments areprovided below.

Claim evaluation component 150 may comprise any combination of hardwareand/or processor-executable instructions stored on a tangible medium.According to some embodiments, claim evaluation component 150 is acomponent of data storage device 140 or of one or more of adjusterterminals 160.

Adjuster terminals 160 may present a graphical representation of theestimated cost and the likelihood associated with each of the pluralityof outcome scenarios. Graphical representations according to someembodiments will be described in detail below. An adjuster may view thegraphical representation and adjust insurance reserves specified withinreserve database server 165. An adjuster may also or alternativelysuggest treatment options, forward a claim to medical personnel, forwarda claim to a claim processing department, and/or re-assign a claim basedon the information presented by the graphical representation.

Medical personnel terminals 170 may receive a claim forwarded byadjuster terminals 160 as described above, and/or may receive agraphical representation of the estimated cost and the likelihoodassociated with each of the plurality of outcome scenarios from claimevaluation component 150. Terminals 170 may be operated by medicalpersonnel to provide medical services associated with the claim based onthe graphical representation. Such services may be facilitated usingmedical applications provided by medical application server 175.

Claim processing terminal 180 may also receive a graphicalrepresentation of the estimated cost and the likelihood associated witheach of the plurality of outcome scenarios from claim evaluationcomponent 150. A claim processor may operate terminal 180 to directmedical and financial processing of the claim based on the informationpresented by the graphical representation. As described above, claimprocessing terminal 180 may also receive a claim for processing from anadjuster terminal 160 based on the information presented by thegraphical representation.

It should be noted that embodiments are not limited to the devicesillustrated in FIG. 1. Each device may include any number of disparatehardware and/or software elements, some of which may be located remotelyfrom one another. Functions attributed to one device may be performed byone or more other devices in some embodiments. The devices of system 100may communicate with one another (and with other non-illustratedelements) over any suitable communication media and protocols that areor become known.

FIG. 2 is a block diagram of computer system 200 according to someembodiments. Computer system 200 may perform the functions attributedabove to categorical aggregation component 130. Computer system 200includes computer processor 201 operatively coupled to communicationdevice 202, data storage device 204, one or more input devices 206 andone or more output devices 208. Communication device 202 may facilitatecommunication with external devices. Input device(s) 206 may comprise,for example, a keyboard, a keypad, a mouse or other pointing device, amicrophone, knob or a switch, an infra-red (IR) port, a docking station,and/or a touch screen. Input device(s) 206 may be used, for example, toenter information into computer system 200. Output device(s) 208 maycomprise, for example, a display (e.g., a display screen) a speaker,and/or a printer.

Data storage device 204 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g.,magnetic tape and hard disk drives), optical storage devices, and/orsemiconductor memory devices such as Random Access Memory (RAM) devicesand Read Only Memory (ROM) devices.

Data storage device 204 stores program instructions for execution byprocessor 200. Categorical aggregation component 210 may comprise a setof such instructions, and may be executed by processor 201 to causesystem 200 to operate as described above with respect to categoricalaggregation component 130 of FIG. 1. This operation may initiallyinclude operation of communication device 202 to retrieve historicalclaim data stored by external data storage device 220. In someembodiments, and as described with respect to FIG. 1, data storagedevice 220 may comprise a data warehouse.

Data storage device 204 also stores categorized and aggregatedhistorical data 212, which may comprise the result of executingcategorical aggregation component 210. Categorized and aggregatedhistorical data 212 may be output via communication device 202 to anexternal storage device such as storage device 220 or storage device 140of FIG. 1. Categorized and aggregated historical data 212 may also oralternatively be output to claim evaluation component 150 or adjusterterminals 160 for consumption thereby according to some embodiments.

FIG. 3 is a flow diagram of process 300 according to some embodiments.Various elements of system architecture 100 and/or computer system 200may execute process 300 according to some embodiments. Process 300 maybe embodied within program instructions of categorical aggregationcomponent 210 of computer system 200, but embodiments are not limitedthereto.

Process 300 and all other processes mentioned herein may be embodied inprocessor-executable program instructions read from one or morecomputer-readable media, such as a floppy disk, a CD-ROM, a DVD-ROM, aZip™ disk, and a magnetic tape, and then stored in a compressed,uncompiled and/or encrypted format. In some embodiments, hard-wiredcircuitry may be used in place of, or in combination with, programinstructions for implementation of processes according to someembodiments. Embodiments are therefore not limited to any specificcombination of hardware and software.

Initially, at 305, historical claim data is received. As describedabove, historical claim data may include patient identificationinformation, a service date, a description, a billing code, a cost, andany other suitable information that is or becomes known. The historicalclaim data may be received from systems of a single operating entity(e.g., a single insurance company), from several operating entities,from relevant literature (e.g., studies, reports, trials), and from anyother claim data source.

Next, at 310, one or more claim categories are identified based on thereceived historical claim data. Each of the one or more claim categoriesis associated with a respective set of claim characteristics. Accordingto some embodiments, the historical claim data is filtered based on oneor more characteristics (described below) prior to identifying the claimcategories. Such filtering may provide improved results and/or speedprocessing by removing less relevant data from the historical data.

According to some embodiments, a claim category is identified at 310 byfirst determining a set of claim characteristics which define claims ofparticular interest. The set of characteristics may be determined basedon scientific literature, the historical claim data, medical expertise,claim adjusting expertise and/or professional actuarial experience. Forexample, the present inventors have discovered that a significantpercentage of non-traumatic back surgery claims exhibit high totalseverity while a significant percentage of non-traumatic back surgeryclaims exhibit low total severity. Accordingly, these types of claimsare believed to form a category of claims which may be amenable tosubsequent analysis according to some embodiments. A set of claimcharacteristics (e.g., billing code, procedure type, co-morbidities,individual claimant data (e.g., age, industry, work type)) which definenon-traumatic back surgery claims is therefore determined at 310.

Next, claims of the historical claim data which exhibit the determinedset of claim characteristics are identified, and it is determinedwhether the number of the identified claims exceeds a statisticalsignificance threshold. This check may be desirable to insure that thenumber of claims is suitable to produce statistically relevantaggregations that may be reliably employed in subsequent analysis.

If the number of claims exceeds the statistical significance threshold,a total severity statistical profile is determined based on a totalseverity of each of the first claims. If the total severity statisticalprofile meets predefined profile criteria, a claim category is createdand is associated with the determined set of claim characteristics. Thetotal severity statistical profile and the predefined profile criteriamay be defined and determined based on known statistical techniques,with the goal of ensuring that the total severities of the identifiedclaims are suitable for analysis as will be described below.

According to some embodiments, one or more subcategories may be definedbased on an identified claim category. Each subcategory of a category isassociated with a set of characteristics which is a subset of the set ofcharacteristics associated with the category. Identification ofsubcategories may proceed according to the above example of categorydefinition. For example, the present inventors have identified thesubcategories “cervical” and “lumbar” of the category “non-traumaticback surgery”, the sub-subcategories “fusion” and “no fusion” of thesubcategory “lumbar surgery”, and the sub-subcategories “fusion” and “nofusion” of the subcategory “cervical surgery”. Each of the categories,subcategories and sub-subcategories correspond to a respective procedurecode in some embodiments, which facilitates the identification ofrelated historical claim data.

Identification of claim categories may therefore proceed iteratively,where a first set of characteristics is initially identified and thenrefined based on the claim data of associated claims. Moreover, anystatistical profile criteria may be used to evaluate the suitability ofa claim category and its underlying claim characteristics.

Claims associated with one of the claim categories are identified at315. Such identification may include comparing claims of the historicaldata with a set of claim characteristics associated with the subjectclaim category. Each identified claim is then assigned to one of aplurality of severity ranges based on its total severity at 320.

The foregoing example uses the severity ranges “Bottom 20%”, “Middle60%” and “Top 20%”, but embodiments are not limited thereto.Accordingly, based on their total severities, each claim of the claimcategory is assigned to one of these three severity ranges at 320.

Next, at 325, an average cost per claim year is determined for theclaims of each total severity range. According to the present example,the determined average cost per claim year is divided amongst expensecategories (e.g., Hospital, Physician, Prescription, Other). FIG. 4illustrates the calculation of an average cost per claim year forhospital expenses for claims of the “Top 20%” total severity range. Asshown, these claims are associated with a claim category “non-traumaticlumbar surgery”. The 1998 total for claim year 2, for example,represents an average hospital cost in 1998 for claims of the Top 20%total severity range for which 1998 was the second claim year.

Returning to process 300, some embodiments further determine, for claimsexisting in each of claim years 2 through X, a ratio between a number ofclaims in the claim year to a number of claims in year 1. These ratiosare determined for each expense category and for each range of totalseverities.

FIG. 5 illustrates calculation of the above-described ratios for each ofclaim years 2 through X, for the “Middle 60%” total severity range andthe hospital expense category. The upper table provides a number ofclaims within the Middle 60% total severity range which did not settle(i.e., arose in a non-settlement jurisdiction and/or exhibited actualmedical closure) and for which a hospital expense was incurred in agiven claim year. Again, this information is determined from thehistorical claim data associated with claims falling under the“non-traumatic lumbar surgery” claim category.

The lower table shows the ratios determined at 330 according to someembodiments. The ratios for a given claim year are determined bydividing a claim count during the claim year by a corresponding claimcount during year 1. For example, the ratio for year 2 (77.8%) is equalto the claim count for claim year 2 (i.e., 1265) divided by the claimcount in claim year 1 for those claims which have matured to at leastyear 2 (i.e., 1810−183=1627). Similarly, the ratio for year 4 (35.0%) isequal to the claim count for claim year 4 (i.e., 432) divided by theclaim count in claim year 1 for those claims which have matured to atleast year 4 (i.e., 1810−183−203−189=1235).

Flow continues from 335 to 340 if any subcategories have beenidentified. For each such subcategory, a determination is made as towhat percentage of the subcategory's claims is associated with each ofthe plurality of total severity ranges. By definition, the claims of theassociated category are segmented according to the severity ranges(e.g., 20%/60%/20%), but at 340 it is determined what percentage of thesubcategory's claims fall in each of the Top 20%, Middle 60% and Bottom20% ranges. For example, it may be determined that, in the “fusion”subcategory of the “non-traumatic lumbar surgery” category, 40% of theclaims are claims which are associated with the Top 20% severity rangeof the “non-traumatic lumbar surgery” category, 52% of the claims areclaims which are associated with the Middle 60% severity range, and 8%of the claims are claims which are associated with the Bottom 20%severity range.

If additional claim categories exist, flow returns to 315 from 345 andcontinues as described above with respect to another claim category.Otherwise, flow terminates.

The information determined at 325, 330 and 340 of process 300 isreferred to herein as categorized and aggregated historical claim data.Embodiments may determine less, more and/or different data than thatdescribed above. The categorized and aggregated historical claim datamay be output and subsequently used to determine claim severities andassociated likelihoods according to some embodiments. Examples of thesedeterminations are set forth in detail below.

FIG. 6 is a block diagram of computer system 600 according to someembodiments. Computer system 600 may perform the functions attributedabove to claim evaluation component 150. Computer system 600 includescomputer processor 601, which is operatively coupled to communicationdevice 602, data storage device 604, one or more input devices 606 andone or more output devices 608. Communication device 602 may facilitatecommunication with external devices. Input device(s) 606 and outputdevice(s) 608 may comprise any devices described above with respect toinput device(s) 206 and output device(s) 208, but are not limitedthereto.

Data storage device 604 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g.,magnetic tape and hard disk drives), optical storage devices, and/orsemiconductor memory devices such as RAM devices and ROM devices.

Data storage device 604 stores program instructions for execution byprocessor 600, such as claim evaluation component 610. Claim evaluationcomponent 610 may be executed by processor 601 to cause system 600 tooperate as described above with respect to claim evaluation component150 of FIG. 1. Communication device 602 may retrieve categorized andaggregated historical claim data stored by external data storage device620 in accordance with this operation. The categorized and aggregatedhistorical claim data may have been generated in accordance with process300 or any other suitable process. In some embodiments, and as describedwith respect to FIG. 1, data storage device 620 comprises a file server.

Data storage device 604 may also store current claim data 612 to beevaluated in view of the categorized and aggregated historical data ofdevice 620. This evaluation may incorporate data from knowledge base616. For example, knowledge base 616 may provide data relating toco-morbidities and or other factors which may affect the total severityof a claim outcome and/or a likelihood of that outcome.

FIG. 7 is a flow diagram of process 700 to evaluate claim outcomesaccording to some embodiments. Process 700 may be embodied withinprogram instructions of claim evaluation component 610 of computersystem 600, but embodiments are not limited thereto.

Current claim data is received at 705. The current claim data may bereceived from any storage device, such as but not limited to a datawarehouse. Some or all of the current claim data may be received from anadjuster via a user interface. FIG. 8 is an outward view of userinterface 800 to receive current claim data according to someembodiments.

According to some embodiments, an adjuster operates computer system 600to execute claim evaluation component 610. In response, output device608 displays user interface 800 to the adjuster.

User interface 800 includes fields that may be pre-populated based oncurrent claim data 612 stored in system 600. The adjuster may also oralternatively populate the fields with data received from disparatesources, such as other claim review databases, physical files, etc.Notably for purposes of the present examples, user interface 800includes fields for indicating co-morbidities (i.e., obesity,drugs/alcohol/psych), surgery type (i.e., “Fusion?”, lumbar), andprojected annual expenditures for various expense categories (i.e.,(H)ospital, (P)hysician, p(R)escription, and Other Medical (OM)). Tablesare also provided for entering other known or expected miscellaneousexposure information that may be used to enhance the subsequentevaluation.

At 710, a pre-defined claim category is identified based on the receivedcurrent claim data. It will be assumed that the pre-defined claimcategory is a claim category determined according to 310 of process 300,but embodiments are not limited thereto. In some embodiments of 710, itis determined that the current claim data exhibits a set ofcharacteristics that is associated with a pre-defined claim categories.With respect to the present example, the pre-defined claim category isthe “non-traumatic lumbar surgery” category described above.

Next, at 715, an estimated cost of the current claims is calculated foreach of a plurality of outcome scenarios based on historical claim dataassociated with the pre-defined claim category. The estimated cost maybe determined based on categorized and aggregated historical data insome embodiments. For example, the estimated cost attributable tohospital expenses for a high-cost (e.g., Top 20% severity) outcomescenario may be determined based on the categorized and aggregatedhistorical data of FIG. 4.

FIG. 9 illustrates graphical representation 900 for purposes ofexplaining some embodiments of 715. Graphical representation 900 may bepresented to an adjuster at 725 of process 700 as will be describedbelow.

In the present example, the annual hospital cost for the high-costoutcome scenario after the first year is determined by first calculatingthe average of the average costs shown in FIG. 4. This average ismultiplied by the factors associated with any existing co-morbidities.Specifically, the average (i.e., $11,161) is multiplied by the obesityfactor (1.1252) and the psych factor (1.1396) to determine an annualcost (i.e., $14,311). This annual cost is used in representation 900unless a greater projected annual spend is received in 705 (e.g., froman adjuster), in which case the projected annual spend is used as theannual cost. The corresponding first year hospital costs are determinedto be equal to the annual cost.

The total attributable to hospital costs is the present value of theannual costs over the listed claim duration (i.e., 34.96 years) and atthe listed discount rate (i.e., 5.00%), plus the first year costs.Embodiments are not limited to the foregoing calculations.

The remaining rows of the Hospital Costs portion of representation 900may be completed as described based on categorized and aggregatedhistorical claim data. However the data used to complete the rowsincludes average costs per claim year for claims in the subject categoryand in the severity range (i.e., Middle 60%, Bottom 20%) correspondingto the row to be completed. The Physician Costs portion, Drug Costsportion, and Other Medical Costs portion of representation 900 may besimilarly completed using similar categorized and aggregated historicaldata, albeit associated with the appropriate expense category andseverity range.

The estimated cost of the current claim for each of the six outcomescenarios is shown in the Total Reserve column of representation 900.The Total Reserve for a given outcome scenario is determined by summingall the Total columns in the row associated with the outcome scenario.Again, embodiments are not limited to the specific calculations setforth herein.

Returning to FIG. 7, a likelihood associated with each of the pluralityof outcome scenarios is determined at 720. The likelihood is determinedbased on the historical claim data associated with the subject claimcategory.

The LHood column of FIG. 9 indicates likelihoods calculated according tosome embodiments of 720. The likelihoods may be calculated based oncategorized and aggregated historical claim data such as thatillustrated in FIG. 5. FIG. 10 illustrates previously-determined ratiosbetween a number of claims in a given claim year to a number of claimsin year 1. These ratios may comprise the ratios corresponding to asingle expense category. In embodiments such as that depicted in FIG.10, the ratio illustrated for a given claim year and severity range isthe maximum of the ratios calculated for each expense category for thegiven claim year and severity range. Accordingly, some but not all ofthe ratios depicted in FIG. 10 are identical to those shown in FIG. 5 inassociation with the hospital expense category.

Continuing with the determination at 720, a probability of closure isdetermined for each severity range. The probability of closure shown inFIG. 10 is calculated as (1−Ratio_(Yr9)/Ratio_(CurrentYr)). Embodimentsare not limited to this calculation.

For scenarios associated with no Natural Medical Closure (i.e.,scenarios 1-3), the likelihoods are calculated for each severity rangeas (% claims_(SeverityRange)(1−Probability of Closure_(SeverityRange))).Conversely, for scenarios associated with a Natural Medical Closure(i.e., scenarios 4-6), the likelihoods are calculated for each severityrange as (% claims_(SeverityRange)(Probability ofClosure_(SeverityRange))). In a case that the current claim isassociated with a subcategory of the identified claim category, thevalues % claims_(SeverityRange) may be those percentages generated at340 of process 300. According to the illustrated example, thepercentages corresponding to the Top 20%, Middle 60% and Bottom 20%severity ranges for the “fusion” subcategory are 40%, 52% and 8%,respectively. Other percentages may be employed in a case that currentclaim is associated with a “no fusion” subcategory.

A graphical presentation of the estimated cost (i.e., Total Reserve) andlikelihood (i.e., LHood) of each outcome scenario is presented at 725.The graphical representation may comprise representation 900, butembodiments are not limited thereto.

FIG. 11 illustrates decision tree 1100 for presenting the costs andassociated likelihoods in a graphical format. Tree 1100 also showsweighted payout amounts obtained by multiplying each associatedlikelihood and payout. The nodes of the tree represent defaultpercentages based on the current claim data and the historical claimdata. In some embodiments, an adjuster or other professional viewingtree 1100 may change these values to reflect his professional judgmentof the claim, resulting in corresponding changes to the values in theLikelihood, Payout and Wt. Payout columns.

The presented graphical representation may improve the evaluation ofpotential claim outcomes. Accordingly, the quality of any actions takenbased on the evaluation may also improve. As shown in FIG. 7, suchactions may include adjusting reserves based on the representation(730), suggesting treatment based on the representation (740) andreassigning the claim based on the representation (750).

The embodiments described herein are solely for the purpose ofillustration. Those in the art will recognize that other embodiments maybe practiced with modifications and alterations limited only by theclaims.

What is claimed is:
 1. A computer system comprising: a data storagedevice for receiving, storing, and providing access to historical claimdata; a computer processor for executing program instructions and forretrieving the historical claim data from the data storage device; amemory, coupled to the computer processor, for storing programinstructions for execution by the computer processor; a categoricalaggregation component comprising program instructions stored in thememory for providing categorized and aggregated historical claim data bydetermining a first set of claim characteristics, identifying firstclaims of the historical claim data which exhibit the first set of claimcharacteristics, determining whether a number of the first claimsexceeds a statistical significance threshold, in response to adetermination that the number of the first claims exceeds a statisticalsignificance threshold, determining a total severity statistical profilebased on a total severity of each of the first claims, determiningwhether the total severity statistical profile meets predefined profilecriteria, and, in response to determining that the total severitystatistical profile meets the predefined profile criteria, associatingthe first set of claim characteristics with a first claim category,identifying claims of the historical claim data associated with thefirst claim category, assigning each identified claim associated withthe first claim category to one of a plurality of total severity rangesbased on the total severity of the identified claim, and determining anaverage cost per claim year divided amongst a plurality of expensecategories for claims of each total severity range when executed by thecomputer processor; and a communication device, coupled to the computerprocessor, to output categorized and aggregated historical claim dataincluding the average cost per claim year for claims of each totalseverity range.
 2. A system according to claim 1, wherein the programinstructions of the categorical aggregation component are for providingcategorized and aggregated historical claim data further by: determininga second set of claim characteristics, the second set of claimcharacteristics being a subset of the first set of claimcharacteristics; identifying second claims of the historical claim datawhich exhibit the second set of claim characteristics, the second claimsbeing a subset of the first claims; determining whether the number ofthe second claims exceeds the statistical significance threshold; inresponse to a determination that the number of the second claims exceedsthe statistical significance threshold, determining a second totalseverity statistical profile based on a total severity of each of thesecond claims; determining whether the second total severity statisticalprofile meets the predefined profile criteria; and in response to adetermination that the second total severity statistical profile meetsthe predefined profile criteria, associating the second set of claimcharacteristics with a second claim category, the second claim categorybeing a subcategory of the first claim category.
 3. A system accordingto claim 1, wherein the first set of claim characteristics comprises atleast one of: one or more medical billing codes, a minimum annual spendrate in one or more medical expense categories, one or moreco-morbidities, and a disability duration.
 4. A system according toclaim 3, wherein the one or more co-morbidities comprise at least oneof: obesity, psychiatric issues, and alcohol use.
 5. A system accordingto claim 1, wherein the one or more claim categories comprise one ormore of: non-traumatic cervical surgery without fusion, non-traumaticlumbar surgery without fusion, non-traumatic lumbar fusion surgery, andnon-traumatic cervical fusion surgery.
 6. A system according to claim 1,further comprising: filtering the historical claim data based on one ormore characteristics prior to identifying the claim categories.
 7. Amethod of operating a categorical aggregation component, the methodcomprising: receiving historical claim data from a data warehouse;determining a first set of claim characteristics; identifying, via aprocessor, first claims of the historical claim data which exhibit thefirst set of claim characteristics; determining, via the processor,whether a number of the first claims exceeds a statistical significancethreshold; in response to a determination that the number of the firstclaims exceeds a statistical significance threshold, determining, viathe processor, a total severity statistical profile based on a totalseverity of each of the first claims; determining, via the processor,whether the total severity statistical profile meets predefined profilecriteria; and in response to a determination that the total severitystatistical profile meets the predefined profile criteria, associating,via the processor, the first set of claim characteristics with a firstclaim category; identifying, via the processor, claims of the historicalclaim data associated with the first claim category; assigning, via theprocessor, each identified claim associated with the first claimcategory to one of a plurality of total severity ranges based on thetotal severity of the identified claim; determining, via the processor,an average cost per claim year divided amongst a plurality of expensecategories for claims of each total severity range; and outputting, to afile server, categorized and aggregated historical claim data includingthe average cost per claim year for claims of each total severity range.8. A method according to claim 7, wherein the first set of claimcharacteristics comprises at least one of: one or more medical billingcodes, a minimum annual spend rate in one or more medical expensecategories, one or more co-morbidities, and a disability duration.
 9. Amethod according to claim 8, wherein the one or more co-morbiditiescomprise at least one of: obesity, psychiatric issues, and alcohol use.10. A method according to claim 7, wherein the first claim categorycomprises one or more of: non-traumatic cervical surgery without fusion,non-traumatic lumbar surgery without fusion, non-traumatic lumbar fusionsurgery, and non-traumatic cervical fusion surgery.
 11. A methodaccording to claim 1, further comprising: filtering the historical claimdata based on one or more characteristics prior to identifying the firstclaim category.
 12. A non-transitory medium storing processor-executableprogram code, the program code executable by a device to: receivehistorical claim data from a data warehouse; determine a first set ofclaim characteristics; identify first claims of the historical claimdata which exhibit the first set of claim characteristics; determinewhether a number of the first claims exceeds a statistical significancethreshold; in response to a determination that the number of the firstclaims exceeds a statistical significance threshold, determine a totalseverity statistical profile based on a total severity of each of thefirst claims; determine whether the total severity statistical profilemeets predefined profile criteria; and in response to a determinationthat the total severity statistical profile meets the predefined profilecriteria, associate the first set of claim characteristics with a firstclaim category; identify claims of the historical claim data associatedwith the first claim category; assign each identified claim associatedwith the first claim category to one of a plurality of total severityranges based on the total severity of the identified claim; determine anaverage cost per claim year divided amongst a plurality of expensecategories for claims of each total severity range; and output, to afile server, categorized and aggregated historical claim data includingthe average cost per claim year for claims of each total severity range.13. A medium according to claim 12, the program code executable by adevice to output categorized and aggregated historical claim datacomprising program code executable by a device to: determine a secondset of claim characteristics, the second set of claim characteristicsbeing a subset of the first set of claim characteristics; identifysecond claims of the historical claim data which exhibit the second setof claim characteristics, the second claims being a subset of the firstclaims; determine whether the number of the second claims exceeds thestatistical significance threshold; in response to a determination thatthe number of the second claims exceeds the statistical significancethreshold, determine a second total severity statistical profile basedon a total severity of each of the second claims; determine whether thesecond total severity statistical profile meets the predefined profilecriteria; and in response to a determination that the second totalseverity statistical profile meets the predefined profile criteria,associate the second set of claim characteristics with a second claimcategory, the second claim category being a subcategory of the firstclaim category.
 14. A medium according to claim 12, wherein the firstset of claim characteristics comprises at least one of: one or moremedical billing codes, a minimum annual spend rate in one or moremedical expense categories, one or more co-morbidities, and a disabilityduration.
 15. A medium according to claim 14, wherein the one or moreco-morbidities comprise at least one of: obesity, psychiatric issues,and alcohol use.
 16. A medium according to claim 12, wherein the firstclaim category comprises one or more of: non-traumatic cervical surgerywithout fusion, non-traumatic lumbar surgery without fusion,non-traumatic lumbar fusion surgery, and non-traumatic cervical fusionsurgery.
 17. A medium according to claim 12, the program code furtherexecutable by a device to: filter the historical claim data based on oneor more characteristics prior to identifying the first claim category.